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arXiv · 2309.08742

RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX

Yohan John·Connor Hughes·Gilberto Diaz-Garcia·Jason R. Marden·Francesco Bullo
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Abstract

To enable the computation of effective randomized patrol routes for single- or multi-robot teams, we present RoSSO, a Python package designed for solving Markov chain optimization problems. We exploit machine-learning techniques such as reverse-mode automatic differentiation and constraint parametrization to achieve superior efficiency compared to general-purpose nonlinear programming solvers. Additionally, we supplement a game-theoretic stochastic surveillance formulation in the literature with a novel greedy algorithm and multi-robot extension. We close with numerical results for a police district in downtown San Francisco that demonstrate RoSSO's capabilities on our new formulations and the prior work.

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Yohan John, Connor Hughes, Gilberto Diaz-Garcia, Jason R. Marden, Francesco Bullo. 2023-09-15. RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX. https://doi.org/10.1109/icra57147.2024.10610477

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